Common Seasonal Pathogens and Epidemiology of Henoch-Schönlein Purpura Among Children

Key Points Question What are the associations of the main seasonal pathogens with the epidemiology of Henoch-Schönlein purpura (HSP)? Findings In this cohort study of 9790 children with HSP and 757 110 children with an infectious disease, time-series analysis of a prospective national surveillance cohort from 2015 to 2023 revealed that 37.3% of HSP incidence was potentially associated with Streptococcus pneumoniae and that 25.6% of HSP incidence was potentially associated with Streptococcus pyogenes. In contrast, all other seasonal pathogens played a minor role. Meaning These findings underscore the potentially significant role of S pneumoniae and S pyogenes in the burden of childhood HSP, suggesting that preventive measures could prove effective for this common form of childhood vasculitis.

The black line shows the monthly incidence of HSP per 100,000 children.The orange line shows the monthly incidence of SARS-CoV-2 infections per 100,000 children.

eFigure 2. Correlograms and Residuals Analysis of the Final Quasi-Poisson Regression Model for the Estimated Fraction of ACS Attributable to Seasonal Pathogens
To assess the quality of the Quasi-Poisson model, we used correlograms (autocorrelation and partial autocorrelation functions which measure the linear relationship between lagged values of a time series) and residuals analysis.Inspection of the correlograms relies on identifying remaining autocorrelation or seasonal pattern of the residuals.The significance of any remaining autocorrelation or seasonality is defined by a correlation higher than +1.96 standard error or lower than -1.96 standard error for each lag of the time series.We checked whether the residuals of the models were normally distributed and had a constant variance over time..99-2.9 (-11.6 to 5.9) .52 -3.0 (-12.0 to 6.0) .52Rotavirus .5.9 (-2.7 to 14.5) .186.9 (-  exp(β 4 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for 12-month period exp(β 5 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for 12 month period θ: 2πkt.t is the frequency of the function ( ) period.exp(β 6 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for semi-annual (6-month) period exp(β 7 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for semi-annual (6-month) period ϕ: 2πkt.t is the frequency of the function (  ) period.exp(β 6 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for semi-annual (6 month) period exp(β 7 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for semi-annual (6 month) period : 2πkt.t is the frequency of the function ( ) period.exp(β 8 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for trimestral (3 month) period exp(β 9 ): estimate of the harmonic functions for fitting a harmonic trend (cosine-sine trend, Fourier regression) for trimestral (2 month) period ω: 2πkt.t is the frequency of the function (

First part:
To estimate the changes in HSP incidence associated with the implementation and relaxation of NPIs.The quasi-Poisson interrupted time series regression model can be written as: Log(Yt) = β0 + β1 * timet + β2 * NPI implementation + β3 * Time after NPI lifting + β 4 * cos(θ) +  5 * sin(θ) +  6 * cos(ϕ) +  7 * sin(ϕ) +ϵt (Equation1) Yt: monthly rate (per 100,000 children) hospitalized for HSP in France.Yt is in a logarithmic base, therefore model's estimates need to be exponentiated to be interpreted as mean percentage changes.exp(β0): estimate of the baseline level (rate per 100,000 children and adolescents) in January 1, 2015 exp(β 1 ): estimate of the slope (trend) in the pre-NPI period.Time t : time elapsed since the beginning of the study.A continuous variable measured in months ranging from January 1, 2015 to March 31, 2023.exp(β 2 ): estimate of the change in level after the NPI implementation.NPI implementation: a categorical variable coded 0 before March 2020 coded 1 afterwards.exp(β 3 ): estimate of change in slope after the NPI lifting Time after NPI lifting: a continuous variable counting the number of months from April 1, 2021 to March 31, 2023.

1 𝑝𝑒𝑟𝑖𝑜𝑑)
, k is the number of sine and cosine pairs (k = 1 for 12

1 𝑝𝑒𝑟𝑖𝑜𝑑
), k is the number of sine and cosine pairs (k = 2 for semi -annual period) The sine and cosine terms fit a seasonal baseline using semi-annual (6 months,  = 2 *  * 2 12